Vital signs monitoring via radio reflections

ABSTRACT

A method for monitoring periodic motions of one or more subjects uses signal reflections from the subjects. The method includes emitting a transmitted signal from a transmitting antenna and receiving a received signal at one or more receiving antennas. The received signal includes a combination of a number of reflections of the transmitted signal, at least some of which are associated with the subjects. The received signal, including the reflections, is processed to determine an estimate of a fundamental frequency of the periodic motions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the National Stage of International Application No.PCT/US2015/027945, filed on Apr. 28, 2015 which claims the benefit of

-   -   U.S. Provisional Application No. 61/985,066 titled “MULTI-PERSON        MOTION TRACKING VIA BODY RADIO REFLECTIONS,” filed on Apr. 28,        2014, and    -   U.S. Provisional Application No. 62/117,087 titled “VITAL SIGNS        MONITORING VIA RADIO REFLECTIONS,” filed on Feb. 17, 2015.

This application is related to, but does not claim the benefit of:

-   -   U.S. Provisional Application No. 61/888,662 titled “MOTION        TRACKING VIA BODY RADIO REFLECTIONS,” filed on Oct. 9, 2013,    -   U.S. Provisional Application No. 61/943,957 titled “MULTI-PERSON        MOTION TRACKING VIA BODY RADIO REFLECTIONS,” filed on Feb. 24,        2014,    -   U.S. Utility application Ser. No. 14/510,263 titled “MOTION        TRACKING VIA BODY RADIO REFLECTIONS,” filed on Oct. 9, 2014.

These applications are incorporated herein by reference.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

This invention was made with government support under CNS-1117194awarded by the National Science Foundation. The government has certainrights in the invention.

BACKGROUND

This invention relates to monitoring vital signs, and more particularlyto monitoring of breathing and/or heart rate via radio reflections.

The monitoring of vital signs (e.g., a human heart rate and breathingrate) of subjects without requiring that a monitoring device be inphysical contact with the subjects is an active area of research.Contemporary approaches for non-contact vital signs monitoring can bedivided into two areas: vision-based techniques and wireless techniques.

For vision based techniques, advances in image processing have allowedresearchers to amplify visual patterns in video feeds (e.g., colorchanges due to blood flow) to detect breathing and heart rate. Suchvideo-based techniques have drawbacks since they require the subject toface the camera and do not work properly when the subject turns awayfrom the camera or is outside the camera's field of view.

For wireless techniques, advances in wireless transmission systems andsignal processing have enabled researchers to monitor vital signs byanalyzing characteristics of wireless signals that have reflected off ofthe subject. Some examples of wireless vital sign monitoring techniquesutilize Doppler radar, WiFi, or ultra-wideband radar. One challenge inusing wireless signals to monitor vital signs is that any motion in theenvironment affects the signal. Since breathing and heartbeats areminute movements, they can be easily masked by interference from anyother source of movement in the environment. Furthermore, the presenceof multiple users, even if the users are stationary, prevents systemsfrom operating correctly since the wireless signal is affected by thecombination of their vital signs, making it difficult to distinguish thevital signs of each individual. Conventional approaches deal with thisproblem by ensuring that there is only one source of motion in theenvironment: namely, the vital signs of the monitored individual. Hence,the experimental setups of the past approaches typically require asingle person to lie still in close proximity to the device.

SUMMARY

Approaches described herein include mechanisms for separating differentsources of motion in an environment. To do so, the approaches build onstate-of-the-art wireless localization techniques that can identify thedistance between the device and different moving objects. The approachesnecessarily use these methods to distinguish the incoming signals basedon distance, rather than estimating the actual location. By doing so,the approaches can distinguish signals reflected off of different bodiesand body parts. The approaches then analyze the motion of the distinctsignals independently to estimate the breathing and heart rate of one ormore individuals.

In some embodiments, approaches described in the applications that areincorporated by reference are used to eliminate reflections from staticobjects (e.g., walls and furniture) and sort reflections from movingobjects into bins (e.g., FFT samples representing ranges of TOF) basedon their location relative to the device (i.e., based on the distance ofthe reflected path from transmitting to receiving antennas). Rather thanor in addition to tracking power reflected from different distances totrack the subtle motions associated with breathing and heartbeat, one ormore embodiments use a model of wireless signals that captures both thereflected power and the phase of the signal.

In some embodiments, harmonic structure of periodic signals, forexample, determined using phase-based processing, is used to determinevital signals, for instance, heart rate and/or breathing rate.

In an aspect, in general, a method for monitoring one or more periodicmotions of one or more subjects using signal reflections from thesubjects includes emitting a transmitted signal including repetitions ofa transmitted signal pattern from a transmitting antenna, receiving, atone or more receiving antennas, a received signal including acombination of a number of reflections of the transmitted signal, atleast some reflections of the number of reflections of the transmittedsignal being associated with the one or more subjects, processing thereceived signal to form time successive patterns of the reflections ofthe transmitted signal from the one or more subjects, processing thetime successive patterns of reflections to form one or more phasesignals including, for each reflection of a subset of the number ofreflections, forming a phase signal representing a variation over timeof a phase angle for the reflection of the transmitted signal in thereceived signal, and processing each phase signal of a subset of the oneor more phase signals to determine an estimate of a fundamentalfrequency of each periodic motion of the one or more periodic motions.

Aspects may include one or more of the following features.

Processing the received signal to form time successive patterns of thereflections of the transmitted signal from the one or more subjects mayinclude forming the subset of the number of reflections includingremoving at least some extraneous reflections of the number ofreflections according to reflection distance.

Forming the subset of the reflections may include, for each reflectionof the number of reflections of the transmitted signal, determiningwhether the reflection is a static multipath reflection, excluding thereflection from the subset of the reflections if the reflection is astatic multipath reflection, and including the reflection in the subsetof the reflections if the reflection is not a static multipathreflection. Determining whether the reflection is a static multipathreflection may include using a time differencing approach.

The method may include determining the subset of the one or more phasesignals including processing each of the one or more phase signals todetermine a measure of periodicity of the phase signal and including thephase signal in the subset of the one or more phase signals if themeasure of periodicity of the phase signal exceeds a predeterminedthreshold. Determining the estimate of the fundamental frequency of eachperiodic motion of the one or more periodic motions may include, foreach periodic motion of the one or more periodic motions, determining apreliminary estimate of the fundamental frequency for the periodicmotion and determining the estimate of the fundamental frequency for theperiodic motion based on the preliminary estimate and a regression ofthe phase signal.

The one or more periodic motions may include periodic motions associatedwith one or more vital signs of a subject. The one or more periodicmotions may include a periodic motion associated with heart beats of asubject. The one or more periodic motions may include a periodic motionassociated with a subject breathing. The one or more periodic motionsmay include a periodic motion associated with an interfering movement ofa subject. The one more periodic motions may include a periodic motionassociated with a subject breathing and further includes a periodicmotion associated with heart beats of the subject.

Processing each phase signal of the subset of the one or more phasesignals to determine an estimate of a fundamental frequency of eachperiodic motion of the one or more periodic motions may includeidentifying a number of spectral peaks in a frequency domainrepresentation of the phase signal, at least some of the spectral peaksbeing located at harmonic frequencies of the estimate of the fundamentalfrequency, and determining the estimate of the fundamental frequencyfrom the identified one or more spectral peaks. Determining the estimateof the fundamental frequency from the identified one or more spectralpeaks may include processing the one or more spectral peaks to determinea number of candidate fundamental frequencies for the periodic motion.

Processing the one or more spectral peaks to determine the number ofcandidate fundamental frequencies for the periodic motion may include,for each spectral peak, determining one or more factors of a frequencyassociated with the spectral peak that are in an expected frequencyrange of the fundamental frequency for the periodic motion, andincluding the determined one or more factors in the number of candidatefundamental frequencies for the periodic motion. The method may includeprocessing the number of candidate fundamental frequencies to determinea preliminary estimate of the fundamental frequency for the periodicmotion.

The method may include determining the estimate of the fundamentalfrequency based on the preliminary estimate of the fundamental frequencyand a regression of the phase signal. The method may include filteringthe phase signal to form a filtered version of the phase signal,including retaining frequency components at the preliminary estimate ofthe fundamental frequency and frequency components adjacent to thepreliminary estimate of the fundamental frequency in the filteredversion of the phase signal, and excluding substantially all otherfrequencies from the filtered version of the phase signal, anddetermining the estimate of the fundamental frequency of the periodicmotion based on a regression of the filtered version of the phasesignal.

Determining the estimate of the fundamental frequency of the periodicmotion based on a regression of the filtered version of the phase signalmay include determining a slope of a phase angle of the filtered versionof the phase signal. Processing the number of candidate fundamentalfrequencies to determine the preliminary estimate of the fundamentalfrequency may include applying a voting algorithm to the number ofcandidate fundamental frequencies. Each phase signal of the subset ofthe one or more phase signals may include a first number of spectralpeaks related to a periodic motion due to heart beats of the subject anda second number of spectral peaks related to a periodic motion due tothe subject breathing.

Identifying the number of spectral peaks in the frequency domainrepresentation of the phase signal may include applying a normalizationalgorithm to distinguish the spectral peaks from a noise floor of thefrequency domain representation. Processing the phase signal of thesubset of one or more phase signals to determine the estimate of thefundamental frequency of each periodic motion of the one or moreperiodic motions may include iteratively processing the phase signal. Atleast some of the subjects may be humans. The subset of the one or morephase signals may include a number of phase signals.

For each phase signal of the subset of the one or more phase signals,determining the estimate of the fundamental frequency for the periodicmotion may include determining a preliminary estimate of the fundamentalfrequency of the periodic motion and determining the estimate of thefundamental frequency of the periodic motion based on the preliminaryestimate of the fundamental frequency of the periodic motion and aregression of the phase signal. Determining the preliminary estimate ofthe fundamental frequency of the periodic motion may include identifyinga frequency associated with a largest peak in a spectral representationof the phase signal and determining the estimate of the fundamentalfrequency of the periodic motion may include filtering the phase signalto form a filtered version of the phase signal, including retainingfrequency components at the preliminary estimate and frequencycomponents adjacent to the preliminary estimate in the filtered versionof the phase signal, and excluding substantially all other frequenciesfrom the filtered version of the phase signal. The estimate of thefundamental frequency of the periodic motion may be determined based ona regression of the filtered version of the phase signal. Determiningthe estimate of the fundamental frequency of the periodic motion basedon a regression of the filtered version of the phase signal may includedetermining a slope of a phase angle of the filtered version of thephase signal.

One or more of the extraneous reflections may correspond to one or moreobjects in the environment. The one or more objects may be moving. Theone or more objects may be periodically moving. The one or more objectsmay include a fan. The one or more objects may be static. The one ormore objects may be vibrating. The vibration of the one or more objectsmay be periodic. Removing extraneous reflections of the number ofreflections according to reflection distance may enable extraction ofperiodic movements of a number of subjects concurrently. Removingextraneous reflections of the number of reflections according toreflection distance may enable eliminating reflections form one or moredifferent body parts of a human.

The one or more different body parts may include a limb. Removingextraneous reflections of the number of reflections according toreflection distance may enables eliminating reflections from one or moredifferent objects on a human's body. The one or more different objectsmay include wearable objects including watches, cellular telephones, andjewelry. After being processed, the received signals from various humanbody parts may be combined to obtain an estimate of the fundamentalfrequencies of the one or more periodic motions with increased accuracy.

In another aspect, in general, a computer-implemented system formonitoring one or more periodic motions of one or more subjects usingsignal reflections from the subjects includes a processor programmed toperform some or all of the steps described above.

Aspects may include one or more of the following features.

The system may include a transmitter for emitting the transmitted signaland a receiver for receiving the received signal.

In another aspect, in general, software stored on a non-transitorycomputer-readable medium includes instructions for causing a processorto perform some or all of the steps of described above.

Aspects may include one or more of the following advantages.

Aspects allow for continuous monitoring of vital signs such as breathingand heart rate without requiring that subjects be in physical contactwith a monitoring device. Since no device needs to be worn, elderlysubjects are less likely to feel encumbered or ashamed by the monitoringdevice. Furthermore, some subjects (e.g., those with dementia) don'tneed to remember to put the device on. Children are unable to remove andmisplace the device. Infants or those with sensitive skin won't developskin irritation from the device.

Due to the non-invasive nature of the vital signs monitoring system,subjects can be continuously monitored (even while at home), allowinghealthcare professionals to study how the subjects' vital signscorrelate with their stress level and evolve with time and age.

Aspects monitor vital signs such as heart rate more accurately thancertain conventional and comfortable technologies such as wristbands.

Aspects can simultaneously monitor the vital signs of multiple subjects.

Aspects can monitor the vital signs of subjects in the presence ofextraneous motion.

Aspects do not require subjects to lie still on a bed facing the device.

Aspects can track subjects' breathing and heart rates with a medianaccuracy of 99%, even when users are 8 meters away from the device, orin a different room.

Aspects have a median accuracy for breathing of 99.3% and for heart rateof 98.5% when a subject is 1 m away from the device. Aspects have amedian accuracy for breathing 98.7% and for heart rate of 98.3% when thesubject is 8 m away from the device.

Other features and advantages of the invention are apparent from thefollowing description, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic block diagram of an embodiment of a vital signsmonitoring system.

FIG. 2A is a plot of transmit and receive frequency over time; and FIG.2B is a plot received energy over frequency corresponding to FIG. 2A.

FIG. 3 is a schematic block diagram of an embodiment of a phase signalextraction module.

FIG. 4 is an exemplary operation of the phase signal extraction moduleof FIG. 3.

FIG. 5 is a schematic block diagram of an embodiment of a phase signalanalysis module.

FIG. 6 is a schematic block diagram of an embodiment of a breathing rateestimation module.

FIG. 7 is a schematic block diagram of an embodiment of a heart rateestimation module.

FIG. 8 is a spectral representation of an exemplary phase signalincluding components related to a subject's breathing and componentsrelated to a subject's heart beat.

FIG. 9 illustrates an exemplary operation of candidate fundamentalfrequency clustering module.

FIG. 10 is a plot of a radius vs. candidate fundamental frequencies.

DESCRIPTION 1 System Overview

Referring to FIG. 1, a vital signs monitoring system 100 monitors thevital signs (e.g., breathing rate and heart rate) of one or moresubjects 102 a, 102 b without requiring any physical contact between theone or more subjects and the vital signs monitoring system 100. Thevital signs monitoring system 100 includes a transmitting antenna 104, areceiving antenna 106, and a signal processing subsystem 108. Note that,in some examples, rather than having a single receiving antenna and asingle transmitting antenna, the system 100 includes a plurality ofreceiving antennas and/or a plurality of receiving antennas. However,for the sake of simplifying the description of the vital signsmonitoring system, the following description refers only to a singlereceiving/single transmitting antenna embodiment.

In general, the vital signs monitoring system 100 transmits a low powerwireless signal into an environment surrounding the system 100 from thetransmitting antenna 104. The transmitted signal reflects off of the oneor more subjects 102 a, 102 b (among other objects such as walls andfurniture in the environment) and is then received by the receivingantenna 106. The received reflected signal is processed by the signalprocessing subsystem 108 to determine breathing rate and heart rateanalysis results 110 for the one or more subjects 102 a, 102 b.

The system 100 exploits the fact that characteristics of wirelesssignals are affected by motion in the environment, including chestmovements due to inhaling and exhaling and skin vibrations due toheartbeats. In particular, as the subjects breathe and as their heartsbeat, a distance between the antennas of the system 100 and the subjects102 a, 102 b (e.g., the chests of the subjects) varies. In someexamples, the system 100 monitors the distance between the antennas ofthe system and the subjects 102 a, 102 b using time-of-flight (TOF)(also referred to as “round-trip time”) information derived for thetransmitting and receiving antennas 104, 106.

For example, in FIG. 1, two paths 112 a and 112 b between thetransmitting antenna 104 and the receiving antenna 106 are shownreflecting off of two representative subjects 102 a and 102 b,respectively. Assuming a constant signal propagation speed c (i.e., thespeed of light), the TOF from a transmitting antenna at coordinates(x_(t), y_(t), z_(t)) reflecting from an subject at coordinates (x_(o),y_(o), z_(o)) and received at a receiving antenna at coordinates (x_(r),y_(r), z_(r)) can be expressed as

$\frac{1}{c}\begin{pmatrix}{\sqrt{\left( {x_{t} - x_{o}} \right)^{2} + \left( {y_{t} - y_{o}} \right)^{2} + \left( {z_{t} - z_{o}} \right)^{2}} +} \\\sqrt{\left( {x_{r} - x_{o}} \right)^{2} + \left( {y_{r} - y_{o}} \right)^{2} + \left( {z_{r} - z_{o}} \right)^{2}}\end{pmatrix}$In this case, with a single pair of antennas, the TOF associated witheach of the paths 112 a, 112 b constrains the location of the respectivesubject to lie on an ellipsoid defined by the three-dimensionalcoordinates of the transmitting and receiving antennas of the path, andthe path distance determined from the TOF. For illustration, a portionof the ellipsoid for each of the paths 112 a, 112 b is depicted usingelliptical lines 114 a, 114 b.

As is described in greater detail below, any movement associated withtwo different subjects that lie on different ellipsoids (i.e., twosubjects that are at different distances from the antennas) can beisolated and analyzed separately.

As is noted above, for each of the subjects 102 a, 102 b, the distanceof the ellipsoid from the pair of transmitting and receiving antennasvaries with to the subject's chest movements due to inhaling andexhaling and skin vibrations due to heartbeats. The varying distancebetween the antennas 104, 106 and the subject is manifested in thereflected signal as a phase variation in a signal derived from thetransmitted and reflected signals over time. Generally, the vital signsmonitoring system 100 extracts a signal representative of this phasevariation from the transmitted and reflected signals and processes thephase variation signal to determine the vital signs of the one or moresubjects.

2 Sin Processing Subsystem

The signal processing subsystem 108 includes a signal generator 116, acontroller 118, a frequency shifting module 120, a phase signalextraction module 122, and a phase signal analysis module 124.

The controller 118 controls the signal generator 116 to generaterepetitions of a signal pattern that is emitted from the transmittingantenna 104. In the embodiment of FIG. 1, the signal generator 116 is anultra wide band frequency modulated carrier wave (FMCW) generator 116.It should be understood that in other embodiments other signal patternsand bandwidth than those described below may be used while followingother aspects of the described embodiments.

The repetitions of the signal pattern emitted from the transmittingantenna 104 reflect off of the subjects 102 a, 102 b and other objectsin the environment, and are received at the receiving antenna 106. Thereflected signal received by receiving antenna 106 is provided to thefrequency shifting module 120 along with the transmitted signalgenerated by the FMCW generator 116. The frequency shifting module 120frequency shifts (e.g., “downconverts” or “downmixes”) the receivedsignal according to the transmitted signal (e.g., by multiplying thesignals) and transforms the frequency shifted received signal to afrequency domain representation (e.g., via a Fast Fourier Transform(FFT)) resulting in a frequency domain representation of the frequencyshifted received signal, S(ω)_(i) at a discrete set of frequencies, ω.

The frequency domain representation of the frequency shifted signal,S(ω)_(i) is provided to the phase signal extraction module 122 whichprocesses S(ω)_(i)) to extract N phase signals, ϕ₁(t), ϕ₂(t), . . . ,ϕ_(N)(t). See, for example, the N=2 outputs, ϕ_(A)(t), ϕ_(B)(t) shown inFIG. 4. In some examples, each of the one or more phase signals, ϕ₁(t),ϕ₂(t), . . . , ϕ_(N)(t), corresponds to one of the ellipsoids in theenvironment where the phase extraction module 122 has detected motion(e.g., the motion of the chest of one of the subjects).

The one or more phase signals, ϕ₁(t), ϕ₂(t), . . . , ϕ_(N)(t), areprovided to the phase signal analysis module 124 which processes each ofthe one or more phase signals, ϕ₁(t), ϕ₂(t), . . . , ϕ_(N)(t), todetermine the breathing rate and heart rate analysis results 110.

2.1 Frequency Shifting

Referring to FIG. 2A, a transmit signal received by the frequencyshifting module 120 includes a series of repeating time intervals 212 ofduration 1T. For each of the time intervals, a transmit frequency isswept over a frequency range as shown by solid line 210. In someembodiments, the frequency range is 5.46-7.25 GHz (i.e., a frequencyrange of approximately 1.8 GHz) with a sweep duration and repetitionrate of 2.5 milliseconds. The receive signal that the frequency shiftingmodule 120 receives from the receiving antenna 106 is a version of thetransmit signal that is delayed by the TOF 222 of the signal (i.e., whenreflected from a single object) and has a frequency as shown in thedashed line 220. Note that the TOF 222 corresponds to a difference 224in transmitted and received frequencies, which is a product of the TOFand the rate of frequency change of the swept carrier for thetransmitting antenna.

Referring to FIG. 2B, if the received reflected signal (dashed line 220in FIG. 2A) is frequency shifted according to transmitted signal (solidline 210 in FIG. 2A) then the result will have energy concentrated atthe frequency difference 224 corresponding to the TOF. (Note that we areignoring the edges of the intervals 212, which are exaggerated in thefigures). The frequency shifting module 120 of FIG. 1 (also referred toas a “downconverter” or a “mixer”) implements the frequency shifting,for example, including a modulator that modulates the received signalwith the transmitted signal and retaining a low frequency rangerepresenting TOF durations that are consistent with the physicaldimensions of the environment.

The output of the frequency shifter is subject to a spectral analysis(e.g., a Fourier Transform) to separate the frequency components eachassociated with a different TOF. In this embodiment, the output of thefrequency shifter is sampled and a discrete time Fourier Transformimplemented as a Fast Fourier Transform (FFT) is computed for eachinterval 212. Each complex value of the FFT provides a frequency samplewith a frequency resolution Δf=1/T_(sweep) where T_(sweep) is the sweepduration (e.g., 2.5 milliseconds).

Continuing to refer to FIG. 2B, it should be recognized that thedistribution of energy over frequency (and equivalently over TOF), isnot generally concentrated as shown in the diagram. Rather, there is adistribution of energy resulting from the superposition of reflectionsfrom the reflective objects in the environment.

2.2 Phase Signal Extraction

Referring to FIG. 3, the FFT output for each time interval, S(ω)_(i) isprovided to the phase signal extraction module 122. The phase signalextraction module 122 processes each FFT output, S(ω)_(i) to extract Nphase signals, ϕ₁(t), ϕ₂(t), . . . , ϕ_(N)(t), each corresponding tomovement in the environment of the system 100 due to the breathing andheart beats of the subjects 102 a, 102 b.

In general, each FFT output, S(ω)_(i) received by the phase signalextraction module 122 includes one or more areas of concentrated energy.Each individual area of concentrated energy is sometimes referred to asa ‘reflection’ since corresponds to a reflection in the signal receivedat the receiving antenna 106. Some reflections are direct, with the pathbeing direct between the reflecting object and the transmitting andreceiving antennas. Other reflections exhibit multipath effects in whichthere are multiple paths from a transmitting antenna to a receivingantenna via a particular reflecting object. Some multipath effects aredue to the transmitted signal being reflected off wall, furniture, andother static objects in the environment. These types of multipatheffects are referred to as static multipath effects.

The phase signal extraction module 122 includes a static multipathremoval module 334, a buffer 333, a phase signal determination module336, and an aperiodic signal removal module 338.

2.2.1 Static Multipath Removal

In some examples, the FFT output, S(ω)_(i) is first provided to thestatic multipath removal module 334 along with one or more previouslyreceived values 335 of the FFT output (e.g., a value of S(ω) that wasreceived j time intervals, or sweeps, ago: S(ω)_(i-j)). The staticmultipath removal module 334 processes S(ω)_(i) and the one or morepreviously received values 335 of S(ω)_(i) to remove static multipatheffects using a time differencing approach. The time differencingapproach used by the static multipath removal module 334 distinguishes amoving object's reflections from reflections off static objects in theenvironment, like furniture and walls. In particular, reflections fromwalls and furniture are much stronger than reflections from a human,especially if the human is behind a wall. Unless these reflections areremoved, they would mask the signal coming from the human and preventsensing her motion. This behavior is called the “Flash Effect’”.

To remove reflections from the static objects (walls, furniture), thestatic multipath removal module 334 leverages the fact that since thesereflectors are static, their distance to the antenna array does notchange over time, and therefore their induced frequency shift staysconstant over time. For each sweep window, S(cu) the static multipathremoval module 334 eliminates the power from the static reflectors bysubtracting the (complex) output of S(ω)_(i) in the given sweep from theoutput of one or more previous sweeps (e.g., S(ω)_(i-j)). This processis called background subtraction because it eliminates all the staticreflectors in the background. In some embodiments, j=1 and the FFToutput from the immediately previous sweep, S(ω)_(i-1) is subtractedfrom the current FFT output S(ω)_(i). In other examples, j is selectedsuch that a previous FFT output is selected using a small time delay(i.e., 2.5 milliseconds previous), while in other embodiments, a greatertime delay may be used (e.g., 12.5 milliseconds, or even over a second,such as 2.5 seconds).

The result of the background subtraction process performed by the staticmultipath removal module 334 is a static multipath free FFT output,S′(ω)_(i) which includes substantially only reflections corresponding tomoving objects in the environment.

2.2.2 Phase Signal Extraction

The static multipath free FFT output, S′(ω)_(i) is then provided to thebuffer 333 which stores a number (i.e., M) of previous static multipathfree FFT outputs, S′(ω)_(i), S′(ω)_(i-1), S′(ω)_(i-2), . . . ,S′(ω)_(i-M). In some examples, the value of M is chosen such that thebuffer 333 represents a predetermined amount of time (e.g., 30 seconds)of the signal received by the receiving antenna 106. In some examples,the buffer 333 is a first-in-first-out (FIFO) buffer that, uponreceiving a new result, S′(ω)_(i), pushes the new result S′(ω)_(i) intothe head of the buffer 333 and evicts an oldest result, S′(w)_(i-M) fromthe end of the buffer 333.

The M static multipath free FFT outputs, S′(ω)_(i), S′(ω)_(i-1),S′(ω)_(i-2), . . . , S′(ω)_(i-M) in the buffer 333 are provided to thephase signal determination module 336 that identifies K reflections thatare present in (substantially all of) the M static multipath free FFToutputs. See, for example, the K=3 reflections A, B, and C in FIG. 4.The phase signal determination module 336 then extracts a “phasesignal,” ϕ_(k)′(t) for each of the identified reflections.

In general, each of the K identified reflections is located in the sameFFT bin in all of the M static multipath free FFT outputs, S′(ω)_(i),S′(ω)_(i-1), S′(ω)_(i-2), . . . , S′(ω)_(i-M). In some examples, for ak^(th) reflection of the K identified reflections, the phase signaldetermination module 336 iterates through the M static multipath freeFFT outputs and, for each static multipath free FFT output, determines aphase angle at the FFT bin associated with the k^(th) reflection. Takentogether, the resulting M phase angles for the k^(th) reflectionrepresent a time progression of the phase for the reflection, or thephase signal, ϕ_(k)′(t).

The phase signal for the k^(th) reflection is related to the distancetraveled from the transmitting antenna 104, to the moving object fromwhich the signal reflected, and back to the receiving antenna 106 asfollows:ϕ_(k)′(t)=2πd _(k)(t)/λwhere λ is the wavelength (e.g., 4.5 cm) of the transmitted signal, andd(t) is the traveled distance from the device to the reflector and backto the device. That is, variations in the distance traveled by thesignal due to inhaling, exhaling, and heartbeats can be identified bymeasuring the resulting variations in the phase of the reflected signal.

2.2.3 Aperiodic Reflection Removal

In some examples, not every reflection that remains after staticmultipath removal necessarily corresponds to the breathing and heartrate of a subject. For example, certain reflections may correspond tolimb motion such as a subject typing, moving their feet, or walking. Theaperiodic signal removal module 338 distinguishes between reflectionscorresponding to breathing and heart rate of a subject and reflectionscorresponding to other types of movement. To do so, the aperiodic signalremoval module 338 exploits the fact that phase signals of reflectionscorresponding to vital signs are periodic while phase signals ofreflections corresponding to other types of movement are generallyaperiodic.

To this end, the aperiodic signal removal module 338 receives the Kphase signals, ϕ₁′(t), ϕ₂′(t), . . . , ϕ_(K)′(t) output by the phasesignal determination module 336 and processes the K phase signals toremove any of the K phase signals that are aperiodic. The output of theaperiodic signal removal module 338 includes N periodic phase signals,ϕ₁(t), ϕ₂(t), . . . , ϕ_(N)(t).

In some examples, to identify the N periodic phase signals, theaperiodic signal removal module 338 processes each of the K phasesignals to determine a measure of periodicity of the phase signal. Ifthe measured periodicity of a given phase signal is above apredetermined threshold, the aperiodic signal removal module 338includes the given phase signal in the set of N periodic phase signals,otherwise the given phase signal is not included in the set of Nperiodic phase signals.

In some examples, to measure the periodicity of a given phase signal,the aperiodic signal removal module 338 computes a Fourier transform(e.g., an FFT) of the phase signal and then evaluates a sharpness of theFFT of the signal. To evaluate the sharpness of the FFT of the phasesignal, the aperiodic signal removal module 338 first identifies a peakvalue of the FFT of the phase signal. If a power associated with theidentified peak value is sufficiently greater than (i.e., above thepredetermined threshold) an average power at the remaining, non-peakfrequencies, then the given phase signal is identified as a periodicsignal. Otherwise, the given phase signal is identified as an aperiodicsignal. The spectral representations of any phase signals that areidentified as being periodic are included in the N periodic phasesignals output by the aperiodic signal removal module 338.

In some examples, measuring the periodicity of phase signals as isdescribed above also ensures that time intervals where a subjectperforms large limb movements are excluded from vital signs analysis.However, time intervals where a subject is performing small movementssuch as typing on a laptop computer or using a cellular telephone arecan still be analyzed for vital signs analysis. In particular, whilesuch small movements may be aperiodic, they do not mask the breathing orthe heart rate since their power does not overwhelm the repetitivemovements due to the subject's vital signs.

The N periodic phase signals, ϕ₁(t), ϕ₂(t), . . . , ϕ_(N)(t) are thenoutput from the phase signal extraction module 122. See, for example,the N=2 outputs, ϕ_(A)(t), ϕ_(B)(t) shown in FIG. 4.

2.2.4 Example

Referring to FIG. 4, an example of the operation of the phase signalextraction module 122 is illustrated for an exemplary FFT output,S(ω)_(i) 488 which includes a number of areas of concentrated energy 490(i.e., reflections). In general, each of the areas of concentratedenergy 332 corresponds to a reflection in the signal received at thereceiving antenna 106. In the exemplary FFT output S(ω)_(i) 488, threeof the areas of concentrated energy (labeled A, B, and C) correspond toreflections due to moving objects that are present in the environment.In this example, reflections A and B are due to periodic vital signmovements of subjects 102 a, 102 b in the environment and reflection Cis due to a non-periodic movement in the environment.

The exemplary FFT output S(ω)_(i) is first provided to the staticmultipath removal module 334 along with one or more previous values ofthe exemplary FFT output S(ω)_(i) (e.g., S(ω)_(i-j)). The staticmultipath removal module 334 uses the background subtraction techniquedescribed above to remove any reflections due to static multipatheffects from the exemplary FFT output S(ω)_(i) resulting in the staticmultipath free FFT output, S′(ω)_(i) 492. In this example, the staticmultipath free FFT output, S′(ω)_(i) 492 includes only the reflectionslabeled A, B, and C which correspond to moving objects in theenvironment.

The static multipath free FFT output, S′(ω)_(i) 492 is provided to thebuffer 333 which stores the static multipath free FFT output, S′(ω)_(i)492 along with M−1 previous outputs of the static multipath removalmodule 334. The buffered static multipath free FFT outputs, S′(ω)_(i),S(ω)_(i-1), S′(ω)_(i-2), . . . , S′(ω)_(i-M) 494 are provided to thephase signal determination module 336 which extracts a phase signal foreach of the reflections in the buffered static multipath free FFToutputs, S′(ω)_(i), S′(ω)_(i-1), S′(ω)_(i-2), . . . , S′(ω)_(i-M) 494.Since there are three reflections (i.e., A, B, and C) in the bufferedstatic multipath free FFT outputs 494, output of the phase signaldetermination module 336 includes three phase signals, ϕ_(A)′(t),ϕ_(B)′(t), ϕ_(C)′(t). A plot 496 of ϕ_(A)′(t) for the example of FIG. 4shows a substantially sinusoidal variation in the phase signal due tobreathing movement. The small perturbations in the phase signal due tothe heart beat movements of the subject are modulated by thesubstantially sinusoidal variation of the breathing movement.

The three phase signals, ϕ_(A)′(t), ϕ_(B)′(t), ϕ_(C)′(t) are provided tothe aperiodic signal removal module 338 which analyzes each of the phasesignals to determine whether it is periodic and then outputs only theperiodic phase signals. Since, as is noted above, C is an aperiodicsignal, the output of the aperiodic signal removal module 338 includesonly ϕ_(A)(t), ϕ(t).

2.3 Phase Signal Analysis

As is illustrated in FIG. 4, a phase signal that varies due to thebreathing and heart beats of a subject includes large peaks and valleysin the phase that correspond to inhalation and exhalation motions of thesubject, respectively. The phase signal also includes smallerperturbations modulated on the large peaks and valleys which are due tothe subject's heartbeats.

Referring to FIG. 5, the N periodic phase signals, ϕ₁(t), ϕ₂(t), . . . ,ϕ_(N)(t) are provided to the phase signal analysis module 124 (shown inFIG. 1) which processes the N periodic phase signals to determine anestimated breathing rate 464 and an estimated heart rate 466 for each ofthe N periodic phase signals.

In some examples, the phase signal analysis module 124 includes a FastFourier Transform (FFT) module 460, a breathing rate estimation module462, and a heart rate estimation module 463. The N periodic phasesignals, ϕ₁(t), ϕ₂(t), . . . , ϕ_(N)(t) are first provided to the FFTmodule 460 which efficiently computes a discrete time Fourier transformfor each of the N periodic phase signals, resulting in N FFT outputs,Φ₁(ω), Φ₂(ω), . . . , Φ_(N)(ω). In some examples, a windowing functionsuch as a Hanning windowing function is applied to the phase signalprior to the FFT module 460 calculating the FFT of the phase signal.Application of such a windowing function reduces unwanted leakage ofstrong frequencies into adjacent bins in the FFT output for the phasesignal.

The N FFT outputs, Φ₁(ω), Φ₂(ω), . . . , Φ(ω) are provided to thebreathing rate estimation module 462 and to the heart rate estimationmodule 463.

2.3.1 Breathing Rate Estimation

Referring to FIG. 6, the breathing rate estimation module 462 receivesthe N FFT outputs, Φ₁(ω), Φ₂(ω), . . . , Φ_(N)(ω) and processes the NFFT outputs to determine the N estimated breathing rates 464.

In some examples, the breathing rate estimation module 462 includes apeak detection module 668, a frequency domain filtering module 670, aninverse fast Fourier transform (FFT) module 672, a phase slopeestimation module, 674, and a breathing rate estimate calculation module676.

To process a given FFT output, Φ_(m)(ω), the FFT output is firstprovided to the peak detection module 668 which identifies an FFT bin inthe FFT output, Φ_(m)(ω) with a peak energy value. The identified FFTbin is associated with a frequency that is an initial, coarse estimateof the subject's breathing rate. However, simply choosing a frequencyassociated with the peak of the FFT output does not provide an accurateestimate of breathing rate since the frequency resolution of the FFToutput is 1/window size. If the window size is, for example, 30 seconds,the resolution of the initial estimate of the subject's breathing rateis ≈0.033 Hz (i.e., 2 breaths/minute). Of course, a finer resolution isdesirable.

Since a single dominant frequency exists in the FFT output, Φ_(m)(ω)(i.e., at the frequency associated with the peak value identified by thepeak detection module 668), a finer resolution can be obtained byperforming a regression (e.g., a linear regression) on the phase of thecomplex time-domain signal associated with the FFT output, Φ_(m)(ω). Todo so, the identified peak FFT bin and the FFT output, Φ_(m)(ω) areprovided to the frequency domain filtering module 670 which filters outall bins of the FFT output, Φ_(m)(ω) except for the identified peak FFTbin and the two FFT bins adjacent to the identified peak FFT bin. Theoutput of the frequency domain filtering module 670 is a filtered FFToutput, Φ′_(m)(ω). In some examples, the filtering performed by thefrequency domain filtering module 670 eliminates noise caused byextraneous and aperiodic movements.

The filtered FFT output, Φ′_(m)(ω) is provided to the inverse FFT module672 which performs an inverse FFT to obtain a complex time-domainsignal, ϕ_(m)′(t) for the filtered FFT output, Φ′_(m)(ω). The complextime-domain signal, ϕ_(m)′(t) is then provided to a phase slopeestimation module 674 which computes the slope, of the complextime-domain signal, ϕ_(m)′(t) as:slope(∠ϕ_(m)′(t))

The slope of the complex time-domain signal (i.e., slope(∠ϕ_(m)′(t))) isprovided to the breathing rate estimate calculation module 676 whichdetermines a fine grained estimated breathing rate 464, in terms ofbreaths per minute, as:

${{{Estimated}\mspace{14mu}{Breathing}\mspace{14mu}{Rate}} = \frac{60 \times {{slope}\left( {{\angle\phi}_{m}^{\prime}(t)} \right)}}{2\;\pi}},$where the factor of 60 transforms the frequency from H: (i.e., 1/second)to breaths/minute.2.3.2 Heart Rate Estimation

Referring to FIG. 7, the heart rate estimation module 463 receives the NFFT outputs, Φ₁(ω), Φ₂(ω), . . . , Φ_(N)(ω) and processes the N FFToutputs to determine the N estimated heart rates 464.

To understand the operation of the heart rate estimation module 463, itis helpful to understand that a typical breathing rate is in a range of8-20 breaths/minute and a typical heart rate is in a range of 40-200beats/minute. Thus, one approach to computing a heart rate estimate fora given one of the FFT outputs, Φ_(m)(ω) is to simply apply a bandpassfilter with a pass band of 40-200 beats/minute to the signal (or to thespectral representation of the signal). However, a challenge in doing sois that the breathing component of the phase signal associated with theFFT output, Φ_(m)(ω) is typically much stronger than the heart ratecomponent of the phase signal. Furthermore, the breathing component isnot a simple sinusoid. Since the breathing component is not a simplesinusoid, it includes a fundamental component at a fundamentalfrequency, f_(breathing) and a number of harmonics at harmonicfrequencies, 2×f_(breathing), 3×f_(breathing), 4×f_(breathing), and soon.

Some of the harmonics fall into the 40-200 beats/minute frequency bandof the heart rate and typically have much higher power than thefrequency components related to the heart rate in the phase signal. Forthis reason, the harmonics of the breathing component of the phasesignal can be confused with frequency components related to the heartrate in the phase signal.

To avoid this confusion, the heart rate estimation module 463 leveragesthe observation that the heart rate component at the fundamentalfrequency f_(heartrate) also has harmonics (i.e., at 2×f_(heartrate),3×f_(heartrate), 4×f_(heartrate), and so on) and that the stronger, loworder harmonics of the heart rate component are collocated in frequencyranges with the weaker, high order harmonics of the breathing ratecomponent (since the power of very subsequent harmonic is lower than theprevious). Any harmonics present at frequencies above the expectedfrequency band for the heart rate (e.g., 40-200 beats/minute) withsignificant power are assumed to be harmonics of the heart ratecomponent and can be used, as is described in detail below, to determinethe fundamental frequency of the heart rate component.

For example, for a breathing rate of 15 breaths/minute, the 4th harmonicof the breathing component in the phase signal (which still hassignificant power) is at 60 breaths/minute and might be confused withthe heart rate's fundamental component (e.g., for a heart rate of 75beats/minute). However, the first harmonic of the heart rate componentin the phase signal (i.e., at 150 beats/minute) is near the 9th harmonicof the breathing component which is very low power (since it is a highorder harmonic). Hence, the heart rate harmonic (i.e., at 150beats/minute) can be used to determine the heart rate.

However, another issue arises in that 150 beats/minute is itself apossible human heart rate. For this reason, the heart rate estimationmodule 463 determines whether the heart rate is 75 beats/minute (i.e.,150 beats/minute is the first harmonic of the heart rate component), ifthe heart rate is 150 beats/minute (i.e., 150 beats/minutes is thefundamental frequency of the heart rate component), or even if the heartrate is 50 beats/minute (i.e., 150 beats/minute is the third harmonic ofthe heart rate component).

To do so, the heart rate estimation module 463 also takes into accounthigher order (e.g., third, fourth, fifth) harmonics of the heart ratecomponent. For example, the heart rate estimation module 463 identifiespeaks in frequency bands of the phase signal that include high orderharmonics of the heart rate component and uses the identified peaks todetermine the heart rate.

To this end, the heart rate estimation module 463 includes a peakenhancement module 778, a peak thresholding module 780, a candidatefundamental frequency identification module 782, a candidate fundamentalfrequency clustering module 784, and a heart rate estimate selectionmodule 786.

To process a given FFT output, Φ_(m)(ω), the FFT output is firstprovided to the peak enhancement module 778 which enhances any peakspresent in the power spectrum of the FFT output, Φ_(m)(ω) in comparisonto any noise present in the FFT bins surrounding the peaks. In someexamples, the peak enhancement module 778 replaces the power in each FFTbin of the FFT output, Φ_(m)(ω) with a value representing thesignal-to-noise ratio (SNR) of the bin with respect to its neighboringFFT bins. For example, the value of each FFT bin in the FFT output,Φ_(m)(ω) is replaced as follows:

${{value}\lbrack{FFT\_ bin}\rbrack} = \frac{{{value}\lbrack{FFT\_ bin}\rbrack}^{2}}{{average}\left( {{value}\left\lbrack {{adjacent}{FFT\_ bins}} \right\rbrack}^{2} \right.}$where adjacentFFT_bins is a range of bins (e.g., 10, 20, 50 bins) oneach side of a bin of interest, FFT_bin.

The output of the peak enhancement module 778 is a peak enhanced FFToutput, Φ_(m)′(ω). The peak enhanced FFT output, Φ_(m)′(ω) is providedto the peak thresholding module 780 which processes the peak enhancedFFT output to identify peaks, P₁, P₂, . . . , P_(Y) in the peak enhancedFFT output that are located in a frequency range above the expectedfundamental frequency range of the heart rate component (e.g., above 200beats/minute) and that exceed a predefined SNR threshold. See, forexample, P₁, P₂, . . . , P₅ in FIG. 8. Any peaks that are identified bythe peak thresholding module 780 are associated with a frequency whichis a candidate for the heart rate component fundamental frequency.

The identified peaks, P₁, P₂, . . . , P_(Y) are provided to thecandidate fundamental frequency identification module 782 which, foreach of the identified peaks, if the frequency associated with the peakfalls within an integer multiple of the 40 to 200 beats/minute frequencyrange for the heart rate component, divides the frequency associatedwith the peak by the integer multiple and adds the result of thedivision as a candidate for the heart rate component fundamentalfrequency. For example, for an exemplary peak associated with afrequency of 220 beats/minute, the frequency falls within the frequencyrange of [2*40 to 2*200], [3*40 to 3*200], and [4*40 to 4*200]. As aresult, 110 beats/minute (i.e., 220 beats/minute/2), 73.3 beats/minute(i.e., 220 beats/minute/3), and 55 beats/minute (i.e., 220beats/minute/4) are added as candidates for the heart rate componentfundamental frequency. The output of the candidate fundamental frequencyidentification module 782 is a set of X candidate fundamentalfrequencies for the heart rate component, C₁, C₂, . . . , C_(X) the setincluding the identified peaks and all candidates determined by thecandidate fundamental frequency identification module 782

The set of X candidate fundamental frequencies, C₁, C₂, . . . , C_(X) isprovided to the candidate fundamental frequency selection module 784which sorts the set of candidate fundamental frequencies by frequency. Awindow (e.g., a rectangular window with a width of 0.8*X) is then slidalong the sorted set of candidate fundamental frequencies. For eachposition of the window as it slides along the sorted set of candidatefundamental frequencies, a number of candidate fundamental frequencies(sometimes referred to as a ich sorts the set of candidate fundamentalfrequea radius of the frequencies in the cluster is computed as:radius=f _(max) −f _(min)where f_(max) is the candidate fundamental frequency with the highestfrequency in the cluster and f_(min) is the candidate fundamentalfrequency with the lowest frequency in the cluster. In some examples,the radius is an indication of how concentrated a set of candidatefundamental frequencies in the cluster are. In general, a set ofcandidate fundamental frequencies in a cluster with a low radius isconsidered to be better than a set of candidate fundamental frequenciesin a cluster with a high radius.

In some examples, each cluster and its associated radius, L₁R₁, L₂R₂, .. . , L_(Z)R_(Z) is passed to the heart rate estimate selection module786 which processes the clusters and their associated radii to determinethe estimated heart rate for the m^(th) phase signal. The heart rateestimate selection module 786 selects the cluster with the lowest radiusand chooses the median frequency in the cluster as the estimated heartrate 464. In some examples, if two or more clusters have the same lowestradius, the heart rate estimate selection module 786 selects the clusterof the two or more clusters with the highest average frequency since theclusters with lower average frequencies are associated withsub-harmonics of the heart rate fundamental frequency.

In some examples, the heart rate estimate selection module 786 computesa confidence ratio for the selected cluster as a ratio between theradius of the selected cluster and the radius of a next best cluster(i.e., a cluster with a radius closest to the radius of the selectedcluster. The heart rate estimate selection 786 stores the estimatedheart rate and its confidence ratio for later use. Then the heart rateestimation module then 463 lowers the signal to noise ratio thresholdused by the peak thresholding module 780 and re-processes the peakenhanced FFT output, Φ_(m)′(ω) to determine another estimated heart rateand confidence ratio. This process repeats until a predefined minimumsignal to noise ratio is reached or until a predefined confidence ratiois reached.

Once the predefined minimum signal to noise ratio or the predefinedconfidence ratio is reached, the estimated heart rate with the highestconfidence ratio is selected as the estimated heart rate for the FFToutput, Φ_(m)(ω). In some examples, the estimated heart rate 464 isoutput from the heart rate estimation module 463.

In other examples, the selected estimated heart rate is treated as acoarse estimate of the fundamental frequency of the heart rate andfurther processing is used to determine a fine-grained estimate from thecoarse estimate.

In particular, as was the case above with the breathing rate, a finerresolution estimate of the fundamental frequency of the heart rate canbe obtained by performing a regression (e.g., a linear regression) onthe phase of the complex time-domain signal associated with the FFToutput, Φ_(m)(ω). To do so, the FFT bin associated with the identifiedcoarse fundamental frequency estimate and the FFT output, Φ_(m)(ω) areprovided to the frequency domain filtering module (not shown) whichfilters out all bins of the FFT output, Φ_(m)(ω) except for theidentified FFT bin and the two FFT bins adjacent to the identified FFTbin. The output of the frequency domain filtering module is a filteredFFT output, Φ_(m)(ω). In some examples, the filtering performed by thefrequency domain filtering module eliminates noise caused by extraneousand aperiodic movements.

The filtered FFT output, Φ_(m)′(ω) is provided to an inverse FFT module(not shown) which performs an inverse FFT to obtain a complextime-domain signal, ϕ_(m)′(t) for the filtered FFT output, ϕ_(m)′(ω).The complex time-domain signal, ϕ_(m)′(t) is then provided to a phaseslope estimation module (not shown) which computes the slope, of thecomplex time-domain signal, ϕ_(m)′(t) as:slope(∠ϕ_(m)′(t))

The slope of the complex time-domain signal (i.e., slope(∠ϕ_(m)′(t))) isused to determine a fine grained estimated heart rate, in terms of beatsper minute, as:

${{{Estimated}\mspace{14mu}{Heart}\mspace{14mu}{Rate}} = \frac{60 \times {{slope}\left( {{\angle\phi}_{m}^{\prime}(t)} \right)}}{2\;\pi}},$where the factor of 60 transforms the frequency from H: (i.e., 1/second)to beats/minute.2.3.2.1 Example of Heart Rate Estimation

Referring to FIG. 8, a plot of a peak enhanced FFT output, Φ_(m)′(ω) foran exemplary FFT output Φ_(m)(ω) includes peaks at a fundamentalbreathing rate frequency of 20 breaths/minute and at a number ofharmonic breathing rate frequencies at integer multiples of 20breaths/minute (i.e., 40 breaths/minute, 60 breaths/minute, 80breaths/minute, and so on). The plot also includes peaks at afundamental heart rate frequency of 85 beats/minute and at a number ofharmonic frequencies at integer multiples of 85 beats/minute (i.e., 170beats/minute, 225 beats/minute, 340 beats/minute, and so on).

The peak enhanced FFT output, Φ_(m)′(ω) is provided to the peakthresholding module 780 of FIG. 7 which applies a threshold 898 to thepeak enhanced FFT output to identify peaks in the peak enhanced FFToutput that are located in a frequency range above the expectedfundamental frequency range of the heart rate component (i.e., above 200beats/minute) and that exceed the predefined SNR threshold 898. In thisexample, there are five peaks, P₁, P₂, . . . , P₅ that are located in afrequency range above the expected fundamental frequency range of theheart rate component and that exceed the predefined SNR threshold 898.P₁ is located at a frequency of 255 beats/minute. P₂ is located at afrequency of 340 beats/minute. P₃ is located at a frequency of 425beats/minute. P₄ is located at a frequency of 510 beats/minute. P₅ islocated at a frequency of 595 beats/minute.

The five identified peaks, P₁, P₂, . . . , P₅ are provided to thecandidate fundamental frequency identification module 784 which, foreach of the identified peaks, if the frequency associated with the peakfalls within an integer multiple of the 40 to 200 beats/minute frequencyrange for the heart rate component, divides the frequency associatedwith the peak by the integer multiple and adds the result of thedivision as a candidate for the heart rate component fundamentalfrequency. In this example, for P₁ which is located at 255 beats/minute,the candidate frequencies 127.5, 85, 63.75, 51, 42.5 are added ascandidates for the heart rate component fundamental frequency. For P₂which is located at a frequency of 340 beats/minute, the candidatefrequencies 170, 113.3, 85, 68, 56.7, 48.6, 42.5 are added as candidatesfor the heart rate component fundamental frequency. For P₃ which islocated at a frequency of 425 beats/minute, the candidate frequencies141.7, 106.25, 85, 70.8, 60.7, 53.1, 47.2, 42.5 are added as candidatesfor the heart rate component fundamental frequency. For P₄ which islocated at a frequency of 510 beats/minute, the candidate frequencies170, 127.5, 102, 85, 72.9, 63.8, 56.7, 51, 46.3, 42.5 are added ascandidates for the heart rate component fundamental frequency. For P₅which is located at a frequency of 595 beats/minute, the candidatefrequencies 198.3, 148.8, 119, 99.2, 85, 74.4, 66.1, 59.5, 54.1, 49.6,45.8, 42.5 are added as candidates for the heart rate componentfundamental frequency.

Referring to FIG. 9, the set of candidate for the heart rate componentfundamental frequencies determined C₁, C₂, . . . , C_(X) is provided tothe candidate fundamental frequency clustering module 784 which sortsthe set of candidate fundamental frequencies by frequency. A window(i.e., a rectangular window with a width of 4 candidates) is then slidalong the sorted set of candidate fundamental frequencies. For eachposition of the window, a radius of the frequencies in the in the window(i.e., a cluster) is computed as:radius=f _(max) −f _(min)where f₁a is the candidate fundamental frequency with the highestfrequency in the cluster and f_(min) is the candidate fundamentalfrequency with the lowest frequency in the cluster. For example, whenthe window 999 in a first position 951, the radius of the cluster isequal to zero. When the window 999 is in a second position 953, theradius of the cluster is equal to 0. When the window 999 is in a thirdposition 955, the radius of the cluster is equal to 3.3. When the window999 is in a fourth position 957, the radius of the cluster is equal to3.8.

Referring to FIG. 10, a plot 1061 of the radius as the window 999 slidesalong the sorted set of candidate fundamental frequencies includes twofrequencies where the radius is at a minimum: 42.5 beats/minute and 85beats/minute.

The clusters and the associated radii determined by the candidatefundamental frequency clustering module 784 are provided to the heartrate estimate selection module 786 which selects the cluster with thelowest radius as being the cluster that includes the fundamentalfrequency for the heart rate. In this case, since two or more clustershave the same lowest radius (i.e., 0), the heart rate estimate selectionmodule 786 selects the cluster of the two or more clusters with thehighest average frequency since the clusters with the lowest averagefrequency are associated with sub-harmonics of the heart ratefundamental frequency. The median frequency of the selected cluster(i.e., 85 beats/minute) is selected as the heart rate fundamentalfrequency and is output as the estimated heart rate 464 for Φ_(m)(ω)

As is noted above, in some examples, the heart rate estimate selectionmodule 786 calculates a confidence ratio for the selected cluster as aratio between the radius of the selected cluster and the radius of anext best cluster. The heart rate estimate selection module 786 storesthe estimated heart rate and its confidence ratio for later use. Thenthe heart rate estimation module then 463 lowers the signal to noiseratio threshold used by the peak thresholding module 780 andre-processes the peak enhanced FFT output, Φ_(m)′(ω) to determineanother estimated heart rate and confidence ratio. This process repeatsuntil a predefined minimum signal to noise ratio is reached or until apredefined confidence ratio is reached.

3 Alternatives

In some examples, rather than analyzing the harmonics of the heart beatcomponent of the phase signal, the heart rate estimation module uses afilter based approach to determine an estimate for the heart rate. Forexample, the heart rate estimation module filters the phase signal usinga bandpass filter with a pass band in a frequency range (e.g., 40 to 200beats/minute) where the heart beat component of the phase signal isassumed to exist. An FFT of the filtered phase signal is calculated anda peak in the FFT output is identified. Note that, in some examples, theabsolute maximum of the FFT output is not chosen as the identified peaksince it is due to leakage from the breathing component of the phasesignal. An inverse FFT of the signal in the FFT bin corresponding to theidentified peak and the two FFT bins adjacent to the FFT bincorresponding to the identified peak is then calculated. A phaseregression is then used to determine the heart rate estimate (as is donefor the breathing rate estimate).

While the above description describes how a single antenna pair is usedto it is noted that a number of antenna pairs can be used to improve theperformance of the system. For example, signals received at a number ofreceive antennas can be combined in various ways to determine an exactlocation of reflecting bodies and can be combined to obtain a cleaner,less noisy phase signal for a reflecting body. In some implementations,a separate phase signal is obtained from each pair of antennas andspectral peaks are identified for each pair of antenna, with the peaksof several pairs being combined to estimate the fundamental frequency,for example, all pairs voting together. In other implementations,different pairs of antennas are processed separately to determineseparate fundamental frequency estimates that are combined.

4 Implementations

Systems that implement the techniques described above can be implementedin software, in firmware, in digital electronic circuitry, or incomputer hardware, or in combinations of them. The system can include acomputer program product tangibly embodied in a machine-readable storagedevice for execution by a programmable processor, and method steps canbe performed by a programmable processor executing a program ofinstructions to perform functions by operating on input data andgenerating output. The system can be implemented in one or more computerprograms that are executable on a programmable system including at leastone programmable processor coupled to receive data and instructionsfrom, and to transmit data and instructions to, a data storage system,at least one input device, and at least one output device. Each computerprogram can be implemented in a high-level procedural or object-orientedprogramming language, or in assembly or machine language if desired; andin any case, the language can be a compiled or interpreted language.Suitable processors include, by way of example, both general and specialpurpose microprocessors. Generally, a processor will receiveinstructions and data from a read-only memory and/or a random accessmemory. Generally, a computer will include one or more mass storagedevices for storing data recordings; such devices include magneticdisks, such as internal hard disks and removable disks; magneto-opticaldisks; and optical disks. Storage devices suitable for tangiblyembodying computer program instructions and data include all forms ofnon-volatile memory, including by way of example semiconductor memorydevices, such as EPROM, EEPROM, and flash memory devices; magnetic diskssuch as internal hard disks and removable disks; magneto-optical disks;and CD-ROM disks. Any of the foregoing can be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

It is to be understood that the foregoing description is intended toillustrate and not to limit the scope of the invention, which is definedby the scope of the appended claims. Other embodiments are within thescope of the following claims.

What is claimed is:
 1. A method for monitoring one or more periodicmotions of one or more subjects using signal reflections from thesubjects, the method comprising: emitting a transmitted signalcomprising repetitions of a transmitted signal pattern from atransmitting antenna; receiving, at one or more receiving antennas, areceived signal comprising a combination of a plurality of reflectionsof the transmitted signal, at least some reflections of the plurality ofreflections of the transmitted signal being associated with the one ormore subjects; processing the received signal to form time successivepatterns of the reflections of the transmitted signal from the one ormore subjects, including forming a subset of the plurality ofreflections including removing at least some extraneous reflections ofthe plurality of reflections according to reflection distance;processing the time successive patterns of reflections to form one ormore phase signals including, for each reflection of the subset of theplurality of reflections, forming a phase signal representing avariation over time of a phase angle for the reflection of thetransmitted signal in the received signal; and processing each phasesignal of a subset of the one or more phase signals to determine anestimate of a fundamental frequency of each periodic motion of the oneor more periodic motions.
 2. The method of claim 1 wherein forming thesubset of the reflections includes, for each reflection of the pluralityof reflections of the transmitted signal, determining whether thereflection is a static multipath reflection, excluding the reflectionfrom the subset of the reflections if the reflection is a staticmultipath reflection, and including the reflection in the subset of thereflections if the reflection is not a static multipath reflection. 3.The method of claim 2 wherein determining whether the reflection is astatic multipath reflection includes using a time differencing approach.4. The method of claim 1 further comprising determining the subset ofthe one or more phase signals including processing each of the one ormore phase signals to determine a measure of periodicity of the phasesignal and including the phase signal in the subset of the one or morephase signals if the measure of periodicity of the phase signal exceedsa predetermined threshold.
 5. The method of claim 1 wherein determiningthe estimate of the fundamental frequency of each periodic motion of theone or more periodic motions includes, for each periodic motion of theone or more periodic motions, determining a preliminary estimate of thefundamental frequency for the periodic motion and determining theestimate of the fundamental frequency for the periodic motion based onthe preliminary estimate and a regression of the phase signal.
 6. Themethod of claim 1 wherein the one or more periodic motions includeperiodic motions associated with one or more vital signs of a subject.7. The method of claim 1 wherein the one or more periodic motionsincludes a periodic motion associated with heart beats of a subject. 8.The method of claim 1 wherein the one or more periodic motions includesa periodic motion associated with a subject breathing.
 9. The method ofclaim 1 wherein the one or more periodic motions includes a periodicmotion associated with an interfering movement of a subject.
 10. Themethod of claim 1 wherein the one more periodic motions includes aperiodic motion associated with a subject breathing and further includesa periodic motion associated with heart beats of the subject.
 11. Themethod of claim 1 wherein processing each phase signal of the subset ofthe one or more phase signals to determine an estimate of a fundamentalfrequency of each periodic motion of the one or more periodic motionsincludes identifying a plurality of spectral peaks in a frequency domainrepresentation of the phase signal, at least some of the spectral peaksbeing located at harmonic frequencies of the estimate of the fundamentalfrequency, and determining the estimate of the fundamental frequencyfrom the identified one or more spectral peaks.
 12. The method of claim11 wherein determining the estimate of the fundamental frequency fromthe identified one or more spectral peaks includes processing the one ormore spectral peaks to determine a plurality of candidate fundamentalfrequencies for the periodic motion.
 13. The method of claim 12 whereinprocessing the one or more spectral peaks to determine the plurality ofcandidate fundamental frequencies for the periodic motion includes, foreach spectral peak, determining one or more factors of a frequencyassociated with the spectral peak that are in an expected frequencyrange of the fundamental frequency for the periodic motion, andincluding the determined one or more factors in the plurality ofcandidate fundamental frequencies for the periodic motion.
 14. Themethod of claim 12 further comprising processing the plurality ofcandidate fundamental frequencies to determine a preliminary estimate ofthe fundamental frequency for the periodic motion.
 15. The method ofclaim 14 further comprising determining the estimate of the fundamentalfrequency based on the preliminary estimate of the fundamental frequencyand a regression of the phase signal.
 16. The method of claim 15 furthercomprising filtering the phase signal to form a filtered version of thephase signal, including retaining frequency components at thepreliminary estimate of the fundamental frequency and frequencycomponents adjacent to the preliminary estimate of the fundamentalfrequency in the filtered version of the phase signal, and excludingsubstantially all other frequencies from the filtered version of thephase signal; and determining the estimate of the fundamental frequencyof the periodic motion based on a regression of the filtered version ofthe phase signal.
 17. The method of claim 16 wherein determining theestimate of the fundamental frequency of the periodic motion based on aregression of the filtered version of the phase signal includesdetermining a slope of a phase angle of the filtered version of thephase signal.
 18. The method of claim 14 wherein processing theplurality of candidate fundamental frequencies to determine thepreliminary estimate of the fundamental frequency includes applying avoting algorithm to the plurality of candidate fundamental frequencies.19. The method of claim 11 wherein each phase signal of the subset ofthe one or more phase signals includes a first plurality of spectralpeaks related to a periodic motion due to heart beats of the subject anda second plurality of spectral peaks related to a periodic motion due tothe subject breathing.
 20. The method of claim 11 wherein identifyingthe plurality of spectral peaks in the frequency domain representationof the phase signal includes applying a normalization algorithm todistinguish the spectral peaks from a noise floor of the frequencydomain representation.
 21. The method of claim 1 wherein processing thephase signal of the subset of one or more phase signals to determine theestimate of the fundamental frequency of each periodic motion of the oneor more periodic motions includes iteratively processing the phasesignal.
 22. The method of claim 1 wherein at least some of the subjectsare humans.
 23. The method of claim 1 wherein the subset of the one ormore phase signals includes a plurality of phase signals.
 24. The methodof claim 1 wherein, for each phase signal of the subset of the one ormore phase signals, determining the estimate of the fundamentalfrequency for the periodic motion includes determining a preliminaryestimate of the fundamental frequency of the periodic motion; anddetermining the estimate of the fundamental frequency of the periodicmotion based on the preliminary estimate of the fundamental frequency ofthe periodic motion and a regression of the phase signal.
 25. The methodof claim 24 wherein determining the preliminary estimate of thefundamental frequency of the periodic motion includes identifying afrequency associated with a largest peak in a spectral representation ofthe phase signal; and determining the estimate of the fundamentalfrequency of the periodic motion includes filtering the phase signal toform a filtered version of the phase signal, including retainingfrequency components at the preliminary estimate and frequencycomponents adjacent to the preliminary estimate in the filtered versionof the phase signal, and excluding substantially all other frequenciesfrom the filtered version of the phase signal, and determining theestimate of the fundamental frequency of the periodic motion based on aregression of the filtered version of the phase signal.
 26. The methodof claim 25 wherein determining the estimate of the fundamentalfrequency of the periodic motion based on a regression of the filteredversion of the phase signal includes determining a slope of a phaseangle of the filtered version of the phase signal.
 27. The method ofclaim 1 wherein one or more of the extraneous reflections correspond toone or more objects in the environment.
 28. The method of claim 27wherein the one or more objects are moving.
 29. The method of claim 28wherein the one or more objects are periodically moving.
 30. The methodof claim 29 wherein the one or more objects includes a fan.
 31. Themethod of claim 27 wherein the one or more objects are static.
 32. Themethod of claim 28 wherein the one or more objects are vibrating. 33.The method of claim 32 wherein the vibration of the one or more objectsis periodic.
 34. The method of claim 1 wherein removing extraneousreflections of the plurality of reflections according to reflectiondistance enables extraction of periodic movements of a plurality ofsubjects concurrently.
 35. The method of claim 1 wherein removingextraneous reflections of the plurality of reflections according toreflection distance enables eliminating reflections form one or moredifferent body parts of a human.
 36. The method of claim 35 wherein theone or more different body parts includes a limb.
 37. The method ofclaim 1 wherein removing extraneous reflections of the plurality ofreflections according to reflection distance enables eliminatingreflections from one or more different objects on a human's body. 38.The method of claim 37 wherein the one or more different objectsincludes wearable objects including watches, cellular telephones, andjewelry.
 39. The method of claim 1 wherein, after being processed, thereceived signals from various human body parts are combined to obtain anestimate of the fundamental frequencies of the one or more periodicmotions with increased accuracy.
 40. A computer-implemented system formonitoring one or more periodic motions of one or more subjects usingsignal reflections from the subjects, the system comprising a processorprogrammed to perform all the steps of any one of claims 1 to
 39. 41.The system of claim 40 further comprising a transmitter for emitting thetransmitted signal and a receiver for receiving the received. 42.Software stored on a non-transitory computer-readable medium comprisinginstructions for causing a processor to perform all the steps of any oneof claims 1 to 39.